In the last couple of years, deep learning algorithms have pushed the boundaries for numerous problems in areas such as computer vision, natural language processing, and audio processing. The performance of advanced machine (deep) learning algorithms has attained the numbers which were unexpected a decade ago. For a given problem, information can be obtained from multiple sources and such multimodal datasets represent information at varying abstraction levels. Combining information from multiple sources can further boost the performance. Recent research has also focused on multimodal deep learning, i.e. representation learning paradigm which learns joint/combined feature from multiple sources. In this relatively new area, information from multiple sources are combined in a deep learning framework. For example, combining audio and video data to obtain joint feature representation.

This special issue focuses on sharing recent advances in algorithms and applications that involve combining multiple sources of information using deep learning. Topics appropriate for this special issue include novel supervised, unsupervised, semi-supervised and reinforcement algorithms, new formulations, and applications related to deep learning and information fusion.

Manuscripts must clearly delineate the role of deep learning information fusion. The manuscript will be judged solely on the basis of new contributions excluding the contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein.